Cluster Computing

, Volume 22, Supplement 5, pp 11101–11108 | Cite as

A novel 3- level energy heterogeneity clustering protocol with hybrid routing for a concentric circular wireless sensor network

  • A. ChithraEmail author
  • R. Shantha Selva Kumari


Big data analytics is an emerging field and wireless sensor network is one of the sources of big data.The sensor nodes in the wireless sensor network have limited energy and necessitate efficient energy utilization. Normally, the sensor nodes are randomly deployed in a square network field and applying clustering technique extends the lifetime of the network. In our proposed work, energy efficient concentric circular clustering protocol(EECCCP), the energy heterogeneity normal and the super nodes having flat topology while the advance nodes having clustering topology, are deployed in different zones of concentric circular network field. A hybrid communication with energy heterogeneity increases the network lifetime. The protocol considers the average energy of the network and residual energy of the nodes to select the cluster-heads. Since the network field is circular with the base station at the center and the nodes with same energy are deployed at equal distance from the base station, this topology increases the network life time and stability of the network. The protocol EECCCP has better performance than stable election protocol, zone stable election protocol and distributed energy efficient clustering.


Direct and clustered communication Three level energy heterogeneity Network lifetime Throughput Wireless sensor network Zone based circular network field 


  1. 1.
    Saeidmanesh, M., Hajimohammadi, M., Movaghar, M.: Energy and Distance Based Clustering. An Energy Efficient Clustering Method for WSNs, vol. 55. World Acadamy of Science, Engineer Technology, Cape Town (2009)Google Scholar
  2. 2.
    Mo, S., Chen, H., Li, Y.: Clustering-based routing for top-k querying in wireless sensor networks. EURASIP J. Wirel. Commun. Netw. 2011, 73 (2011)CrossRefGoogle Scholar
  3. 3.
    Romer, K., Marten, F.: The design space of wireless sensor networks: a survey. IEEE Wirel. Commun. Netw. 11(6), 54–61 (2004)CrossRefGoogle Scholar
  4. 4.
    Singh, S.P., Sharma, S.C.: A survey on cluster based routing protocols in wireless sensor networks. Proc. Comput. Sci. 45, 687–95 (2015)CrossRefGoogle Scholar
  5. 5.
    Saravanakumar, R., Susila, S.G., Raja, J.: Energy efficient homogeneous and heterogeneous system for wireless sensor networks. Int. J. Comput. Appl. 17(4), 33–38 (2011)Google Scholar
  6. 6.
    Gupta, Vrinda, Pandey, Rajoo: An improved energy aware distributed unequal clustering protocol for heterogeneous wireless sensor networks. Eng. Sci. Technol. Int. J. 19, 1050–1058 (2009)CrossRefGoogle Scholar
  7. 7.
    Sirsikar, S., Wankhede, K.: Comparison of clustering algorithms to design new clustering approach. Sci. Direct Proc. Comput. Sci. 49, 147–154 (2015)CrossRefGoogle Scholar
  8. 8.
    Vijayan, K., Raaza, A.: A novel cluster arrangement energy efficient routing protocol for wireless sensor networks. Indian J. Sci. Technol. 9(2), (2016).
  9. 9.
    Vivek, K., Chand, N.: Clustering algorithms for heterogeneous wireless sensor network: a survey. J. Appl. Eng. Res. 1(2), 273 (2010)Google Scholar
  10. 10.
    Smaragdakis, G., Matta, I., Bestavros, A.: SEP: A Stable Election Protocol for Clustered Heterogeneous Wireless Sensor Networks, pp. 1–11. Boston University, Massachusetts (2010)Google Scholar
  11. 11.
    Heinzelman, W.R.: Energy-efficient Communication protocol for Wireless micro sensor networks. In: Proceedings of 33rd Annual Hawaii Inter Cord on System Sciences, Hawaii, USA: IEEE Computer Society, (2000)Google Scholar
  12. 12.
    Bala, Manju, Awasthi, Lalit: Proficient D-SEP protocol with heterogeneity for maximizing the lifetime of wireless sensor networks. J. Intell. Syst. Appl. 7, 1–15 (2012)Google Scholar
  13. 13.
    Faisal, S., Javaid, N., et al.: Z-SEP: zonal-stable election protocol for wireless sensor networks. J. Basic Appl. Sci. Res. 3(5), 132–139 (2013)Google Scholar
  14. 14.
    Qing, L., Zhu, M., et al.: Design of a distributed energy-efficient clustering algorithm for heterogeneous wireless sensor networks. Comput. Commun. 29(12), 2230–7 (2006)CrossRefGoogle Scholar
  15. 15.
    Saini, P., Sharma, A.K.: E-DEEC-Enhanced Distributed Energy Efficient Clustering Scheme for heterogeneous WSN. 1st International Conference on Parallel, Distributed and Grid Computing. (2010)Google Scholar
  16. 16.
    Bandyopadhyay.S., Coyle, E.J.: An energy efficient hierarchical clustering algorithm for wireless sensor networks. In: Proceeding of INFOCOM (2003)Google Scholar
  17. 17.
    Heinzelman, W.R., Chandrakasan, A.P., Balakrishnan, H.: An application-specific protocol architecture for wireless microsensor networks. IEEE Trans. Wirel. Commun. 1(4), 660–670 (2002)CrossRefGoogle Scholar
  18. 18.
    Selvi, M., Velvizhy, P., Ganapathy, S., Khanna, A., Nehemiah, H.K., Kannan, A.: A rule based delay constrained energy efficient routing technique for wireless sensor networks .Clust. Comput. 1–10 (2017)Google Scholar
  19. 19.
    Ciznicki, Milosz, Kurowski, K., Weglarz, J.: Energy aware scheduling model and online heuristics for stencil codes on heterogeneous computing architectures. Clust. Comput. 20(3), 2535–2549 (2017)CrossRefGoogle Scholar
  20. 20.
    González, H., Fraguela, B.: A general and efficient divide-and-conquer algorithm framework for multi-core clusters. Clust. Comput. 20(3), 2605–2626 (2017)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  1. 1.Department of ECEK.L.N.C.I.TSivagangaiIndia
  2. 2.Department of ECEMepco Schlenk Engineering CollegeSivakasiIndia

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